Watch keynotes live from AWS re:Invent 2018

I want to receive marketing communications from Amazon Web Services, Inc. and its affiliates about their offerings. You may unsubscribe here. We will handle your information in accordance with our Privacy Notice.

Formula One Group Uses Amazon SageMaker to Optimize Racing

Beginning a Transformation

Formula One Group is moving most of its infrastructure from on-premises data centers to AWS and standardizing on AWS machine-learning services—including Amazon SageMaker.

Optimizing Racing with Machine Learning

Using historical race data collected from cars over the past 65 years, Formula 1 data scientists are training deep-learning models that make race predictions and help teams optimize mid-race decisions. The models can predict when teams should pit their cars, determine the best timing for changing tires, and evaluate how drivers are performing.

Bringing Fans onto the Track

Formula 1 then uses AWS data streaming, analytics, and media services to deliver insights about driver decisions and car performance to its more than 500 million fans.

Building for the Future

Because Formula 1 runs its high-performance computing workloads in a scalable environment on AWS, the organization can innovate on the Formula 1 racing experience, car design, and more without worrying about capacity.

Financial Services

Intuit Makes Tax Filing Less Taxing Using AWS

Learn how financial technology company Intuit uses Amazon SageMaker to train its machine learning models quickly and at scale, cutting the time needed to deploy models by 90 percent.

Embracing the Cloud

Intuit is all in on AWS and uses a wide breadth of AWS services to provide the elasticity it needs to handle highly seasonal traffic patterns. Since 2013, Intuit has moved its infrastructure, applications, data, and machine learning capabilities to AWS.

Product Innovation Through Data Science

Intuit explores machine learning as it seeks to make arduous tasks, like filing taxes, easy and even delightful for its customers.

Amazon SageMaker for Machine Learning

Using Amazon SageMaker, Intuit has reduced the cost and the time needed to deploy machine learning models. Data scientists can now create a model and scale it out to many servers, and what used to take six months now takes one week.

Powering Prosperity

By empowering its data scientists, Intuit continues to develop and enhance products to serve its mission: to power prosperity for its customers around the world.

Machine Learning

MLB Chooses AWS as Official Machine Learning Provider

Learn How America’s Professional Baseball League Brings Meaning to Statistics Using AWS Machine Learning

A Foundation for Deep Learning

MLB has been collecting statistical data on its players and clubs for decades, and in 2015 it started using AWS to collect and distribute game-day stats to enhance the fan experience.

Empowering Developers

By using Amazon Sagemaker, MLB is empowering its developers and data scientists to quickly and easily build, train, and deploy machine-learning models at scale.

Lightening the Load

These models help MLB eliminate manual, time-intensive processes associated with recordkeeping and statistics, like scorekeeping, capturing game notes, and classifying pitches.

Personalizing the Game

MLB plans to work with the Amazon ML Solutions Lab to continue improving Statcast—its tracking technology that analyzes player performance—including testing accuracy of pitch predictions and creating personalized viewer experiences.

A Home Run with Artificial Intelligence

MLB will continue to innovate using artificial intelligence. The organization plans to use Amazon Comprehend to build a language model that could create scripts for live games that simulate iconic announcers.

SERVERLESS

Matson Operates Its Global Shipping and Logistics Businesses on AWS

Learn how Matson is using AWS to drive innovation and world-class customer service, while achieving operational reliability, security, and infrastructure cost savings.

Real-Time Container Tracking

Matson built a flagship mobile application for global container tracking that allows customers to perform real-time tracking of their freight shipments. Other valuable features in the application include interactive vessel schedule searching, location-based port map lookups, and live gate-camera feeds.

iRobot Delivers on the Promise of Robots in the Smart Home with AWS

iRobot Roomba 900 Completes Cleaning

The Roomba 900 series completes a cleaning mission in the home and returns to the dock for charging.

Data Processing

iRobot processes the home map, calculates the total floor space cleaned and the status code for the cleaning mission, and publishes the metadata to AWS IoT.

Data Streams Available

iRobot uses an AWS IoT rule to put the message into an Amazon Kinesis stream. From Kinesis, iRobot can process the cleaning mission data. Kinesis allows multiple teams to receive the stream of data.

Data Storage and Analysis

AWS Lambda receives the cleaning mission metadata and parses the format to Amazon DynamoDB. Amazon Kinesis batches the mission data and stores it in Amazon S3. Amazon S3 is used as the iRobot data lake for analytics, where all message data is compressed and stored. Once the data is in Amazon S3, iRobot uses the AWS Analytics toolset. Amazon Athena allows iRobot to explore and discover patterns in the data without having to run compute resources all the time.

Data Correlation

The cleaning mission is stored in Amazon DynamoDB and linked to a specific robot and consumer.

Customer Notification

The consumer gets an alert that informs them of a successful Roomba 900 series cleaning mission.

ENTERPRISE APPLICATIONS

BP Improves Effectiveness and Gains Cost Agility and Speed for Its Critical Business Apps

See how BP simplified and modernized its suite of SAP applications, improving user experience while gaining cost agility and enhanced performance.

Managing Critical Business Apps

BP's IT organization manages SAP applications used by thousands of employees worldwide for supply chain, procurement, finance, and more.

Improving Speed & Cost Agility

To improve speed and gain cost agility, BP used Amazon EC2 to migrate these core business apps to the cloud. In addition, the team built EC2 X1 instances to increase scale and to power their real-time analytics.

Increasing Performance

The team can now stand up systems on demand in hours instead of weeks or months. BP is seeing performance increases across the board, including a 40 percent speed improvement for the Lubricants ERP system.

Learn How Our Customers Build on AWS

Global Network of AWS Regions

The AWS Cloud spans 57 Availability Zones within 19 geographic Regions around the world, with announced plans for 15 more Availability Zones and five more Regions in Bahrain, Cape Town, Hong Kong SAR, Milan, and Stockholm